System and data collection method

ABSTRACT

Disclosed is a system of collecting and refining information that may include requesting user inputs and recording user inputs to provide outputs of high purity and high value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/139,122 filed with the United States Patent andTrademark Office on Mar. 27, 2015, the entire contents of which isherein incorporated by reference.

BACKGROUND

1. Field

Example embodiments relate to methods of collecting and refiningintelligence and a system for collecting and refining data.

2. Description of the Related Art

Data collection methods are relatively common in the art, however, mostmethods do not provide anonymity. Furthermore, most systems do notprovide a motivation to encourage users to engage in the data collectionprocess. Finally, traditional methods do not provide dataauthentication.

SUMMARY

Example embodiments relate to methods of collecting and refining dataand a system for collecting and refining data. In example embodiments,the inventive methods obtain data that is more accurate, more timely,and more relevant than data obtained by traditional methods. As aresult, better data is generated more efficiently and for a lower price.Furthermore, through the collection and refining process new market datais created of a type that has never existed previously.

In accordance with example embodiments, a method of collecting data mayinclude asking a question to a plurality of users and obtaining analpha, beta, and zeta input from each user. The method may also includeobtaining just one or any combination of the before mentioned inputs.The method may also include an operation of computing a payout for eachuser based on a distribution of the alpha data set, a user's beta input,and a user's zeta input.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a view of a system in accordance with example embodiments;

FIG. 2 is a view of a method in accordance with example embodiments;

FIG. 3 is a view of a method in accordance with example embodiments;

FIG. 4 is a view of a method in accordance with example embodiments;

FIG. 5 is a view of a method in accordance with example embodiments;

FIG. 6 is a view of a method in accordance with example embodiments;

FIG. 7 is a view of a method in accordance with example embodiments;

FIG. 8 is a view of a method in accordance with example embodiments;

FIG. 9 is a view of a method in accordance with example embodiments

FIG. 10 is a view of a method in accordance with example embodiments;

FIG. 11 is a view of a method in accordance with example embodiments;

FIG. 12 is a view of a method in accordance with example embodiments;

FIG. 13 is a view of a method in accordance with example embodiments;

FIG. 14 is a view of a method in accordance with example embodiments;

FIG. 15 is a view of a method in accordance with example embodiments;

FIG. 16 is a view of a method in accordance with example embodiments;

FIG. 17 is a view of a method in accordance with example embodiments;and

FIG. 18 is a view of a method in accordance with example embodiments.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings, in which example embodiments of the inventionare shown. The invention may, however, be embodied in different formsand should not be construed as limited to the example embodiments setforth herein. Rather, these example embodiments are provided so thatthis disclosure will be thorough and complete, and will fully convey thescope of the invention to those skilled in the art. In the drawings, thesizes of components may be exaggerated for clarity.

In this application, it is understood that when an element or layer isreferred to as being “on,” “attached to,” “connected to,” or “coupledto” another element or layer, it can be directly on, directly attachedto, directly connected to, or directly coupled to the other element orlayer or intervening elements that may be present. In contrast, when anelement is referred to as being “directly on,” “directly attached to,”“directly connected to,” or “directly coupled to” another element, thereare no intervening elements present. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

In this application it is understood that, although the terms first,second, etc. may be used herein to describe various elements and/orcomponents, these elements and/or components should not be limited bythese terms. These terms are only used to distinguish one element,component, region, layer, and/or section from another element,component, region, layer, and/or section. Thus, a first element,component region, layer or section discussed below could be termed asecond element, component, region, layer, or section without departingfrom the teachings of example embodiments.

Spatially relative terms, such as “beneath,” “below,” “lower,” “above,”“upper,” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the structure in use or operation in addition to theorientation depicted in the figures. For example, if the structure inthe figures is turned over, elements described as “below” or “beneath”other elements or features would then be oriented “above” the otherelements or features. Thus, the exemplary term “below” can encompassboth an orientation of above and below. The structure may be otherwiseoriented (rotated 90 degrees or at other orientations) and the spatiallyrelative descriptors used herein interpreted accordingly.

Embodiments described herein will refer to planform views and/orcross-sectional views by way of ideal schematic views. Accordingly, theviews may be modified depending on manufacturing technologies and/ortolerances. Therefore, example embodiments are not limited to thoseshown in the views, but include modifications in configurations formedon the basis of manufacturing process. Therefore, regions exemplified inthe figures have schematic properties and shapes of regions shown in thefigures, and do not limit example embodiments.

The subject matter of example embodiments, as disclosed herein, isdescribed with specificity to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different features orcombinations of features similar to the ones described in this document,in conjunction with other technologies. Generally, example embodimentsrelate to methods of collecting data and analyzing data and a system forcollecting and analyzing data.

FIG. 1 is a view of a system 100 in accordance with example embodiments.In example embodiments, the system 100 includes a user interface 10 anda network system 20. The user interface 10 may provide a means for auser to interface with the network system 20. For example, the userinterface 10 may be a personal device, a handheld device, a gamingconsole, or other electronic device capable of network interaction anduser input, for example, a smart phone, configured to communicate withthe network system 20. In example embodiments the user interface 10 maycommunicate with the network system 20 either directly or indirectly,for example, through the cloud. For example, in some embodiments dataand other information may be exchanged over a wireless network, a wirednetwork, and/or a combination of the two.

In example embodiments the network system 20 may include variousconventional elements. For example, the device network system 20 mayinclude at least one electronic database 25 that may be used to storevarious types of data. For example, in one nonlimiting exampleembodiment, the electronic database 25 may store a plurality ofquestions which may be provided to a user via the user interface 10. Theinvention, however, is not limited to one electronic database as exampleembodiments also provide for a network system having a plurality ofelectronic databases for storing data. For example, in anotherembodiment, the at least one electronic database 25 may include aplurality of electronic databases storing various types of data, forexample, various types of questions which may, or may not be, arrangedin categories, and other more sophisticated methods of arrangement. Ofcourse, in example embodiments the electronic databases may also be usedto store data, for example, answers to various questions provided by auser interfacing with the network system 20 via the interface 10. Ofcourse, the network system 20 may include various other elements, forexample, nontransitory computer readable-mediums having instructionswhich may be used by the microprocessors of the network system 20 tocontrol the network system 20. In this application, non transitorycomputer readable-mediums comprise all computer-readable mediums exceptfor a transitory, propagating signal Further, the electronic databasesmay store additional types of data such as various corrected user inputvalues that may be used to calculate correlation factors or demographiccorrections (to be explained shortly).

In example embodiments, the network system 20 may store additional data.For example, in one nonlimiting example embodiment, at least oneelectronic database 25 may store data associated with a particular knownor anonymous user. For example, in example embodiments, transactionhistories for users may be stored in the at least one electronicdatabase 25. Examples of these histories may include, but are notlimited to, notification histories, open venture histories, closedventure histories, and currency transaction history. As yet anotherexample, the at least one database 25 may store additional informationsuch as, but not limited to, product data in the event the networksystem 20 is configured to provide a user an option to purchase, redeem,or otherwise acquire one or more product items.

In example embodiments the network system 20 may further include atleast one processor 30, for example, an electronic microprocessor. Inexample embodiments the processor 30 may be configured to performvarious functions such as collecting, processing and refining data. Forexample, in one nonlimiting example embodiment the processor 30 may beconfigured to perform various operations such as, but not limited to,providing a series of questions to a user, receiving a series of inputsfrom the user, coordinating storage of the plurality of inputs,calculating a payout, and calculating various parameters based onanswers provided by various users, correlating and refining inputs anddata sets based upon user data and other relevant metrics.

In one nonlimiting example embodiment, the system 100 may be used tocollect data from various users. In this nonlimiting example embodiment,the system 100 may provide various questions to users and may collectvarious inputs from the users. The system 100 may also allow users toventure currency which may be, but is not required to be, real currency,digital currency, or crypto currency. For example, in one nonlimitingexample embodiment the system 100 may ask a user a question and the usermay venture a certain amount of currency on whether the user answered aquestion correctly. In the event the user speculates correctly, the usermay earn currency which may be used to purchase, redeem, or otherwiseacquire various items from a store. In one nonlimiting example, productsmay be available for purchase, redemption, or acquisition in an on-linestore managed, at least in part by, system 100. In another nonlimitingexample, items may be available for purchase, redemption, or acquisitionwith said currency outside of the system 100. In example embodiments,information regarding prizes in the store may be stored in theelectronic database 25 and the processor 30 may be configured tofacilitate purchase of items from the store within the system 100 oroutside the system 100.

In example embodiments, the system 100 may implement a novel method forcollecting, analyzing, and refining data. In example embodiments,subsystems or byproducts of the system may include or use intelligentgrowth correlated currency, incentives, feedback, anonymity, userauthentication, data authentication, logical gameplay progression,intelligent user profiling, user corporations, digital banks and debt,leveling up/accreditation, taxes, timer, product placement andadvertising, psychological modeling, and so on.

In example embodiments the system 100 may provide a virtual environmentin which a currency system is established. The currency, which may ormay not be crypto currency, which may or may not be an intelligentgrowth correlated central currency, and which may be hard or softcurrency, may allow users of the system 100 to purchase various itemsfrom a store, for example, an online store. In addition, the system 100may allow users to risk their currency in an effort to increase anamount of currency a user has. In one nonlimiting example embodiment,for example, a user may be provided a series of questions and for eachquestion a user may provide a series of answers/inputs. If the userbelieves they can accurately estimate a popular position as reflected inan alpha data set, the user may venture some of his/her currency. If theuser is correct, the user may earn additional currency. If the user isincorrect, the user may lose a ventured amount of currency. The system100 incentivizes questions with outcomes that are not easily estimated,or stated differently, questions with an alpha data set that representsa more even distribution. In other words, alpha data sets having arelatively even distribution may have a greater payout than whosedistributions are not.

FIG. 2 is a flowchart illustrating a method of collecting data inaccordance with example embodiments. The invention may be carried outvia the device network system 100. For example, the processor 30 may beencoded with instructions, or may have access to instructions, toimplement the steps of FIG. 2. Referring to FIG. 2, the method beginswith a user accessing the system 100 using the user interface 10. Inexample embodiments, the user may have a login which is not associatedwith any personally identifiable information. As such, the user of thesystem 100 may be anonymous on the system. Logging into the system 100may start the game play. In one nonlimiting example, user login is notrequired for system 100. In example embodiments, the user may be offeredseveral options for accessing various questions associated with theowner's system 100. For example, the system 100 may provide varioustopics which may be selected by a user. The topics, for example, mayinclude current events, history, sporting events, politics, etc. Theuser may also be offered groups of questions related to subtopics. Forexample, if history is selected, the user may select American history,African history, or European history. As another example, if sportingevents was selected the user may select questions related to a specificsport such as, but not limited to, basketball, baseball, football, orsoccer. In the alternative, the user may be offered another category ofquestions such as, but not limited to, popular questions, endingsoonest, etc. In this application, “ending soonest” refers to questionswhich is available for only a limited amount of time and which may beunavailable in a relatively short time in comparison to other questions.Of course, in example embodiments, the system 100 may allow a user toimmediately answer questions without selecting a topic or subtopic ofquestions, as such, the steps of selecting a topic, selecting asubtopic, and selecting sort function, ending soonest, popular, etc.,may be omitted. Regardless, the system 100 in example embodiments mayprovide the user access to intelligent groups of related questions afterthe user has entered into the system 100.

In example embodiments the questions presented to a user may be eitherdefinitive or non-definitive in nature. For example, a question may askhow many states are in the United States. Such a question is definitivein nature. However, the question may, instead of being definitive, benon-definitive. For example, the system 100 may ask the user “ShouldPuerto Rico be made a state?” This question has no right or wrong answerand may be an example of a non-definite question.

In example embodiments questions may be repeated from time to time. Forexample, the system 100 may provide the user a question related to a sexof a person. For example, the system 100 may ask the question “Are youmale or female?” The system may record this information and then re-askthe question at a later date. Re-asking the question may serve toauthenticate or validate a user. Furthermore, the date the question isasked and answered may be recorded so that the user's answer may beretrieved at a later date and compared to the user's later answer whenthe question is repeated.

In example embodiments, the method according to example embodiments mayinclude having the system 100 present a user a series of questions andhaving the user provide data inputs to the system 100. For example, inFIG. 2, the system 100 may provide a group of questions having Numquestions therein. In example embodiments, Num may define only a fewquestions, for example, one, two or three questions, or severalquestions, for example, dozens or even hundreds of questions. In themethod of FIG. 2, the user is presented a question and the user has theopportunity to provide an alpha input, beta input, and zeta input foreach question. This data may be recorded in an electronic database, forexample, the electronic database 25 of FIG. 1. In example embodiments,the alpha data is personal to the user, the beta data represents whatthe user believes is true of a group of users, and the zeta data isrelated to currency which may be risked by the user. For example, thenumber of questions (Num) provided a user may be three questions and thethree questions may be: 1) “Do most people rent or own theirresidence?;” 2) “Which navy has the most submarines: A) Russia; B)United States; or C) China?;” and 3) “What item is most valuable in anemergency: A) Shelter; B) Weapon; C) Cell phone; D) Money; or E) Food?”In example embodiments a user may provide the alpha input, beta input,and zeta input for each question. For example, if the user believes mostpeople rent, the user may communicate this to the system and this datamay be stored as an alpha input. If the user believed that most peoplebelieved that most users who provided alpha inputs selected “own,” thenthe user may select “own” as their beta input and communicate this tothe system 100. This input may be stored as a beta input. If the userbelieved their beta input was the same as the most popular alpha input,the user may risk a certain amount of currency, for example ten (10)Zeta (assuming Zeta is a name for the currency used by the system 100).If the user was correct in selecting the beta input which correspondedto the most popular alpha input for all users, the user may receive apayout, if not, the user may lose his/her risked currency.

The embodiment of FIG. 2 is not meant to limit the invention. Forexample, as shown in FIG. 10, rather than having a method in which anumber of questions NUM is provided to a user, the method may include anoperation in which a user is provided an option of requesting anotherquestion. If the user requests another question, as shown in FIG. 10,the system may ask an additional question and the user may provideadditional input.

In example embodiments, the questions may be disclosed to a plurality ofusers and the system 100 may record the alpha, beta, and zeta inputsfrom each of the users. For example, in example embodiments, the alpha,beta, and zeta inputs may be recorded for each user in the electronicdatabase 25. It is understood that the architecture described in FIG. 1is for purposes of illustration only and is not intended to limit theinvention. For example, whereas FIG. 1 illustrates only a singleelectronic database 25, it is under stood symbol 25 may actuallyrepresent a plurality of electronic databases across which data isstored in one or more locations performing one or more independentfunctions. Similarly, although FIG. 1 illustrates a single processor 30,it is understood symbol 30 may represent a plurality of processors forimplementing the methods of example embodiments.

In example embodiments, a question may be provided to a plurality ofusers and the plurality of users may generate a plurality of individualalpha inputs, a plurality of individual beta inputs, and a plurality ofindividual zeta inputs for the question. In this application, acollection of individual alpha inputs generated by the plurality ofusers is referred to as an alpha data set and a collection of betainputs generated by the plurality of users is referred to as a beta dataset. In example embodiments, the alpha data set may be a basis forcalculating the payout for speculation and a user's beta input may be abasis for awarding a payout for speculation. The zeta input is theamount of currency the user has risked based upon their beta input. Theplurality of all zeta inputs for a question is called the zeta data set.The greater the amount of currency risked, the greater the amount ofpotential gain and/or loss. Thus, Zeta input may be an indicator as tohow confident a user is in their Beta input. Inputs include userselection/preference and the amount of time for user to makeselection/preference. In other words, the system 100 of exampleembodiments may not only record the alpha, beta, and zeta inputs fromeach player, but the amount of time it took to make each respectiveselection.

Other factors may also affect a user's/player's payout. For example, inone nonlimiting example embodiment, the system 100 may keep track of,for example, in an electronic database, all of or some of the users' networth of zeta and may use a user's net worth to modify a payout. Forexample, those with a higher net worth may have a lower payout thanthose having a lower net worth despite providing the same alpha, beta,and zeta inputs as a higher net worth player. As another example, thesystem 100 may also keep track of how often a user logs into the systemand may adjust the user's payout based on the number of times the userhas logged into the system 100. For example, the system 100 may giveusers who have a higher participation rate a higher payout than thosewith a lower participation rate on a particular question despite havingthe same alpha, beta, and zeta inputs for the particular question. Asanother example, the system 100 may track how many questions a user hasanswered and may adjust the payout based on the number of questions auser answered. For example, a first user who answered more questionsthan a second user may receive a higher payout on a question than thesecond user despite having the same alpha, beta, and zeta input providedthe first user answered more questions than the second user.

Table 1 is an example of a collection of data obtained from six (6)users of Applicant's system 100: Bill, Hillary, George, Laura, Ronald,and Nancy. In Table 1, each user was asked three (3) different questions(in this case, Num=3). The first question is: Do most people rent orown? The second question is: Which navy owns the most submarines: A)Russia; B) United States; or C) China? The third question is: what itemis the most valuable item in an emergency: A) Shelter; B) Weapon; C)Cell phone; D) Money; or E) Food?

Question 1 Question 2 Question 3 User Alpha Beta Zeta (Z) Alpha BetaZeta (Z) Alpha Beta Zeta (Z) Bill Rent Own 50 B C 10 A A 20 Hillary RentOwn 50 B C 10 B A 20 George Own Own 100 C C 50 B A 5 Laura Own Own 50 AA 50 B A 5 Ronald Own Rent 25 B B 25 D A 50 Nancy Own Rent 25 B B 25 D A50

In this particular nonlimiting example, the alpha data set for thisgroup would be {Rent, Rent, Own, Own, Own, Own} for question 1, {B, B,C, A, B, B} for question 2, and {A, B, B, B, D, D} for question 3. TheBeta data sets for this nonlimiting example are {Own, Own, Own, Own,Rent, Rent} for question 1, {C, C, C, A, B, B} for question 2, and{A,A,A,A,A,A} for question 3. The Zeta data sets for this nonlimitingexample would be {50,50,100,50,25,25} for question 1, {10, 10, 50, 50,25, 25} for question 2, and {20,20,5,5,50,50} for question 3.

In this particular nonlimiting example, payouts for individual playerswill be a function of at least two parameters: 1) whether the individualplayer's beta input reflected the most popular alpha input within thealpha data set; and 2) the actual distribution of results in the alphadata set (or a type of refined alpha input, which will be explainedlater). For example, in the nonlimiting example of Table 1, the mostpopular answer as to whether individuals rent or own their properties,in the alpha data set, was “Own.” Thus, because each of Bill, Hillary,George, and Laura's beta input matched the most popular alpha input inthe alpha data set, each of Bill, Hillary, George, and Laura may win acertain amount of currency, the amount thereof being dependent on thedistribution of answers if the first alpha data set. Similarly, forquestion 2, the most popular alpha input in question 2's alpha data setwas “B” (corresponding to the United States having the largest navy). Inthis particular example, because Ronald and Nancy were the only oneswith a beta input which matched the most popular alpha input in question2's alpha data set, only Ronald and Nancy may win a certain amount ofcurrency (the amount thereof being dependent on the distribution ofanswers if the first Alpha data set). Similarly, for question 3, themost popular alpha input in the alpha data set is B (corresponding to aweapon). However, because none of Bill's, Hillary's, George's, Laura's,Ronald's, and Nancy's beta input matched the most popular answer in thealpha data set (i.e. shelter (A)), none of Bill, Hillary, George, Laura,Ronald, and Nancy may receive a payout and each may lose an amount ofcurrency related to their zeta input.

Actual payout calculations may be defined by an owner of the system 100.For example, one nonlimiting payout algorithm may expressed as theequation

P=(1−PW)/PW

Where P=payoutPW=winning percentage.For example, for question 1, the payout P=(1-4/6)/(4/6)=0.5 whereas thepayout P for question 2 would be P=(1-3/6)/(3/6)=1.0. Thus, Bill's zetawould increase by 25 zeta (0.5*50) whereas George's zeta would increaseby 50 zeta (0.5*100) based on their answers to question 1 and the amountof zeta ventured. Of course, other equations may be used to calculate apayout. For example, an owner of the system 100 may decide to augmentthe above Payout calculation with a global payout multiplier. Thislatter nonlimiting payout algorithm may be expressed as P=(G/PW)*(1−PW)where G is the global payout multiplier. The global payout multipliermay be any number selected by the owner of the system 100, for example,1 or 2.

In example embodiments it is envisioned that the distribution of alphadata may, from time to time, produce a tie for a most popular answer.For example, in the preceeding example, if Hillary had chosen A insteadB as the alpha input for question 3, then the alpha data set forquestion 3 may have been {A, A, B, B, D, D}. In this latter case, A, B,and D are tied as the most popular answer in this alpha data set. Inorder to break this tie, the system 100 may be configured to send anotification to the owner of the system 100 and the owner may input oneor more alpha inputs to break the tie. In the alternative, the system100 may be programmed with a function to randomly pick the most popularanswer in an alpha data set where there is a tie. That is, amongst thealpha data tied for a most popular answer, the randomize function maybreak the tie between the most popular answers. For example, a randomizefunction of the data set {A,B,D}, the most popular answer in the dataset {A, A, B, B, D, D}, may randomly pick either A, B, or D as the mostpopular data in the alpha data set and the payout may be based on theanswer selected by the randomizer. The system 100 may also be configuredto award a payout to all of the answers that were tied as most popular.That is, if the popular position to a given question was 50%/50% bothpotential answers would receive a payout.

In at least one example embodiment, the system 100 may be configured toprovide users a predetermined amount of currency the first time a userplays. For example, in one embodiment, a user may be provided tenthousand zeta. The system 100 may be further configured to provide theuser with additional zeta each time a game is played. For example, inanother embodiment, the system 100 may provide a user one thousand zetaevery time a game is played. In these latter embodiments it is possiblefor a user to obtain a great deal of “free” currency for using thesystem 100. In order to prevent a user from acquiring too much “free”currency, the system 100 may be further configured so as not to providea user “free” currency when the user's account contains more than apredetermined amount of currency. For example, in another embodiment, auser who has an account containing more than ten thousand zeta will notbe awarded “free” zeta each time a game is played until which time thatuser's currency total may drop below ten thousand zeta. In yet anotherembodiment, the system 100 may implement a “gifting program” in which aset a dollar amount (for example, five dollars) may be given to usersfor simply signing onto their system. A certain amount of this money,for example, half of the money, may be given to the user in zeta. Zeta,in the inventor's system, may have an exchange rate which may, or maynot, vary over time. For example, if the exchange rate when a user logsin is ten to one, then the user may be given twenty five zeta whichcorresponds to $2.50. The remaining money may be given over time. Forexample, the remaining $2.50 may also be converted to zeta and thendispersed over time. For example, in one embodiment, the remainder maybe dispersed over the next five times a user logs in, provided thelogins are separated by a preset amount of time, for example twenty fourhours. In example embodiments, this “gifting program” may be implementedonly in cases where a user's net worth in zeta is below a thresholdvalue. The threshold value, however, may be calculated by the system andmay be a function of various parameters such as, but not limited to, thedollar amount of prizes available to a user or the amount of zeta in thesystem. As such, the threshold value may change over time and may berelatively high at one point in time and relatively low in another pointof time. Of course, the exchange rate may also be calculated by thesystem and may also vary as a function of the amount of zeta in thesystem and/or the dollar amount of goods available.

In a nonlimiting example, user may speculate all available currencywhich is held in an electronic escrow until which time a question ends,payout is computed, and user's account balance is adjusted. If the userhas no available currency to risk the user may still provide alpha,beta, and zeta inputs. However, the zeta input will default to zero.This will be noted in the zeta input and zeta data set as the user hadno available currency which may represent a different intent than a zetainput of zero if the user had currency available to speculate.

However, in at least one example embodiment, the system 100 may beconfigured to provide a virtual banking/lending system so that a usermay actually borrow currency and risk the borrowed currency, in otherwords, acquire debt and leverage available currency, pay interest andrisk potential insolvency or default. In another embodiment one playermay simply borrow currency from another player. However, in this latternonlimiting embodiment, any transfer of currency between players mustpass through a processing algorithm. In other words, the system 100 mayact as a central authority to approve a transfer of currency from oneplayer's account to another's, provide fraud protection, and correct fordiscrepancies in the system. In another example embodiment the currencymay be used outside of the system 100 as a currency for the exchange ofgoods. For example, in one nonlimiting example embodiment, a user coulduse zeta to buy products on an online company, for example, amazon.comor paypal.

The inventor has also contemplated a scenario where a fraudulent partyenters into the system and poses as an authentic/legitimate party. Inthis case, the fraudulent party may attempt to purchase various goodsusing the authentic/legitimate party's currency. In order to preventthis from happening, the inventor contemplates a fraud protection systemwhere the system 100 is configured to detect fraud or minimize theimpact of fraud. For example, suppose the authentic/legitimate playerlogs into the system 100 and determines a number of unauthorizedtransactions have taken place. In this case, the authentic/legitimateplayer may alert system 100 of the fraud and the system 100 may correctthe product purchases and adjust the authentic/legitimate player'saccount holdings. This method may be enabled, for example, byintroducing a time lag (for example, one, two, or three days) between apurchase order placed by a user and a purchase order placed by thesystem 100 to an actual supplier of a goods. In the interim, the system100 may send a message to the second player alerting him/her of thepurchase order giving them a chance to terminate the order. In thisevent, the system 100 may terminate the order and adjust theauthentic/legitimate player's currency holdings.

It is understood that questions may or may not have the same payout andthe payout may not be calculated until all alpha inputs for a questionare collected from a plurality of users. In example embodiments, thepayout may be based upon the distribution of the alpha inputs within thealpha data set. In one embodiment, the closer to an even distribution,the higher the payout. Said another way, the payout may be higher for ayes/no question with a 51%/49% distribution than a yes/no question witha 98%/2% distribution. This may incentivize the answering of questionswhere the outcome may not be easily estimated, because these arepotentially the questions that most users are most interested in and thequestions that may have the most valuable data sets. In exampleembodiments, the inventor's system 100 and methods may allow for thepotential for all users who provided alpha, beta, and zeta inputs to aspecific question to “win” at the same time or lose at the same time, orall receive a payout or all users lose risked currency, respectivelyThis aspect of example embodiments differs from the current state of theart which dictates generally that winners can only win in relation towhat losers lose, a generally balanced system.

In example embodiments the currency used in the system may be digital.For example, in one example embodiment, digital currency, called Zetaand represented by the

symbol, may be earned by using the system 100 and answering questionsand speculating on outcomes. Rather than having a balanced system wherea user can only win what others lose, the system 100 of exampleembodiments may use a dynamic currency system where all players can winat the same time or lose at the same time. In one embodiment, if theamount of currency in circulation increases then the price of prizes inthe store may increase. Or if the available goods decreases and currencyin circulation stays the same then the price of the goods may increase.This is different than the current state of the art where winners canonly win what the others lose. It is emphasized that in at least oneexample embodiment, the system 100 may function as a central authorityhaving the ability to create and destroy currency. Since the system 100may be configured to create currency, there is a potential for allplayers to risk currency and win currency. In this case, the totalamount of currency managed by system 100 may increase. On the converse,it is also possible that all players may risk and lose currency. In thislatter case, the overall amount of currency managed by system 100 mayreduce.

In example embodiments, the device network system 20 of system 100 mayhave an online store and currency utilized by system 100 may be used byusers to purchase products from the online store. In one exampleembodiment, a vendor may pay money to owner of system 100 to hostvendor's product in the online store. The money paid by the vendor maygo into a pool of funds which includes similar monies paid by othervendors. Some of the money may go to paying operating expenses and theremainder may be available to users to purchase, redeem, or acquireproducts in the online store. For example, if there is

10,000,000 in circulation in the system and $1,000,000 in prizes, a $6deck of playing cards may cost

60, the $6 then being debited from the pool of funds available for prizepurchases and being placed in an escrow account to be paid to vendorupon request, or some other method of remittance as the owner deems fit.If there was $500,000 in the pool of money for prizes, a $6 deck ofplaying cards may cost

120.

In a practical implementation of example embodiments, a vendor mayprovide additional compensation to the owner of the system 100 to havetheir product featured within additional areas of the system outside ofthe online store, such as a pause screen. In this example embodiment,when a user selects a product from the online store they may receive theproduct directly from the vendor. User may pay all tax, shipping, andother applicable fees. The system 100 according to example embodimentsmay not stock any products. Rather, the system 100 may generate avoucher for the purchase under $600. This voucher may utilize a couponcode entered at checkout. The system 100 may then be billed by vendorfor the amount. The owner may then provide funds to the vendor from theescrow account holding funds for purchased products. The owner of thesystem 100 and vendor may have negotiated the cost of the product at thetime of contract execution before the product is in the store whichincludes both resale and wholesale price. In one embodiment, the user ofthe system 100 may pay vendor wholesale price for the product. So, auser may buy a $60 product which may not mean that the pool of prizemoney is debited $60. If the wholesale price of that product was agreedto be $30 then the prize money pool may only be debited $30.

cost, however, may be based upon retail cost. The system owner 100 maythen be able to deduct the $30 from its taxes as a business expense.Vendor may be able to deduct up-front payment and ongoing per-clickpayments.

For the purchase or acquisition of a product with a retail value of overa certain amount, for example, $600, there may be tax implications.Users may need to provide name, address, and social security number sothe system 100 can send users tax information. Out of state residentsmay find that these winnings are also taxable in their home state.However, the out of state resident may claim an out-of-state tax creditfor the tax paid to the other state.

In use, there may be a sales tax exemption for property purchased foruse as a prize to players and it may not be subject to a state's salestax, for example, Iowa sales tax. The winner of the prize may not beobligated to pay the sales tax either. This exemption may include thepurchase of a motor vehicle subject to registration. Note: winners ofgift certificates may be required to pay sales tax when they makepurchases using the gift certificates.

Example embodiments of system 100 may utilize a novel process forcollecting user demographic information. Instead of requiring users toprovide information up-front at time of registration or beginningservice, the system 100 may populate demographic information through theprocess of gameplay. For example, during game play, the question “Whatis your sex? male/female?” may be asked and the user's response may berecorded and used to populate the player's demographic information.Users may skip questions, so this may not necessarily mean that thisinformation will be provided by the user. However, a user may (andprobably would) like to know if there are more males/females using thesystem 100 so they may answer the question because, in at least oneexample embodiment, if a user doesn't answer a question the user cannotsee the outcome, which can also be described as the distribution ofrefined data as represented in the alpha data set. In exampleembodiments, there may also be currency penalties for skippingquestions.

In example embodiments, when a user provides an alpha input to aquestion the user may be able to see the outcome of the alpha data setas a function of percentage, for example, 62% YES/38% NO, users,however, in example embodiments, may not be able to see how manyresponses there were. The inventor believes the ability to see how auser's peers view issues is a strong incentive for answering questions.As described above, in at least one embodiment, if a user skips aquestion and does not provide an Alpha input, the users sees nothing—nofeedback regarding that question. Users do not see the B data set or anyother data. However, it is envisioned that users may “level up” andaccordingly may have more detailed and sophisticated informationavailable.

In example embodiments the alpha data set may include refined data. Thesystem 100 may utilize a novel data refining system utilizingcorrelation and authentication, among other processes to ensurerepresentative data samples. User data inputs may provide demographicdata on a user. It is important to note, again, that users, in at leastone embodiment, may be anonymous on the system 100 and all demographicdata may be linked to an alias with no personally identifiableinformation. By using this information and the information from otherpreviously answered questions the system 100 may be able to account fornon-genuine users, fake users/bots, and other non-desirable user traits.For example, certain questions may be asked periodically on a recurringbasis. If the user provides differing answers to the same question overtime, this may be a red flag. If a user demonstrates non-uniformquestion response times, this may also be a red flag. For example, if auser answers questions at 1 s, 1 s, 1 s, 16 s, 1 s . . . it may beobvious that the user may not reading the questions, or more accurately,may be scanning the questions for a particular question or topic.Furthermore, this user may have only speculated on the fourth questionwhere they spent 16 s. The inventor does not believe that this usershould be removed from the system. Obviously, in an embodiment where auser is penalized for skipping a question, this user may have determinedthere may be a penalty for skipping a question, but no penalty for justclicking an answer to a question they are not interested in. Theseinputs from this user may provide flawed information, for example,flawed demographic information, and skew the alpha data sets for thequestions they did not answer thoughtfully. For these reasons only thefourth question may be counted for this user. Counted means that thisalpha input may be used in the distribution for payout computation.Furthermore, the system 100 may be able to provide varying correcteduser input values to the user inputs. For example, User H may have acorrected user input value of 1.7 applied to their input while User Kmay have a corrected user input value of 0.3 applied to their input. Itis also anticipated that certain users will believe that, statistically,they have a higher likelihood of speculating on the more popular answerif they always have the same A and B inputs, regardless of what theyindividually believe. The methods of example embodiments may not removeusers from the system, however, the system may disregard their alphainputs towards the alpha data set and therefore the distribution forinputs deemed unreliable. Their beta inputs however, may be valuable andwell thought out. Furthermore, these “AB” same users may havedemonstrated a certain intelligence and strategic capability and mayrepresent the type of user that the system may focus on “leveling up” tostop this type of play. The “leveling up” of users may be not onlyimportant to keep users interested but to also allow for better datacollection in the system.

In example embodiments, the system 100 for data collection andauthentication is vastly superior to the current state of the art. Notonly is the information more accurate and specific, but the informationis timely. With the system one may be able to tell if a user had a childand when, if the child was a boy/girl, if a user was divorced and when,if a user bought a house and when, if it was their first house, if auser bought a car and when, if it was a hybrid, if it was Japanese, etc.In at least one example embodiment, a system guarantees a level ofanonymity since, in this embodiment, a player uses an anonymous loginand the system does not store any personally identifiable information.In this latter embodiment users are not allowed to leave comments,photos, or any posts that may compromise identity. Furthermore, in atleast one example embodiment, user speculation history may be deletedfrom account after a period of time which may be more or less than 24hours and Alpha data inputs are not viewable in any user accounts.

In example embodiments, questions may be intelligently designed andlinked to build upon each other. In the same way that an intelligentlydesigned security clearance examination will seek to understand whethera person is reliable, trustworthy, of good conduct and character, andloyal to the U.S., the system 100 may seek to understand individuals ina sophisticated way.

In example embodiments a user may exchange currency in the game forproducts in a store, which may be an online store, in or outside thesystem 100. The store may host a variety of products, the quality ofwhich may be determined by the system operator/owner. For example, inone embodiment, an operator of the system 100 may require products inthe store be: 1) the best of their kind; 2) made in America; and 3)satisfy any other personal requirements set forth by the systemoperator. In one embodiment, the store may only have one product permarket segment. For example, if the market segment is sunglasses,several vendors, for example, American Optical, Randolph Engineering,Oakley, etc. may all bid for one spot in the store. This may be donemonthly via a closed or open bid process.

In example embodiments, these product requirements may be carefully andspecifically selected so as to create a store with products that will beattractive to the initial niche of users that the system is targeting.It may also be important to cultivate a luxury affluent image of thesystem 100. At a certain point when this niche has been fully utilized,products may shift focus towards a new niche, for example, professionalwomen, then another niche, etc. At some point there may be a necessityfor increased revenue from vendors. At this point more lucrative marketsegments may be targeted, such as luxury autos, cell phones, etc., opento manufacturers from all regions. However, the luxury/quality/best ofkind and personal approval requirements may still exist.

In example embodiments products may be “bought” by the users, so acompany, for example, Oakley, may only pay $17,000 for their product tobe hosted in the store but move $23,000 worth of goods. It isanticipated that the store may grow to encompass a relatively largenumber of products and market segments, for example, approximately 250products in 250 market segments, representing the best of the best and“the place” to have your new product for launch. It is understood,however, that the store may encompass more or less than approximately250 products in 250 market segments.

In at least one example embodiment, the system 100 may make available adollar amount, for example, about $10, per user per year in productsawarded at full scale. This may begin at a relatively low level, forexample, about $2 per user per year, and may increase thereafter. Eventhis value of $2 per user per year is well above and beyond any existingincentive system. Most importantly, this incentive system scales. Themore users the more products, the more products the more users, the moreusers the more products, and so on, and so on. Furthermore, it ispostulated that when a user is in the mindset that they are using thesystem 100 to be entertained, be educated, and to shop they are muchmore receptive to the product information and much more likely to make apurchase. As such, a user may be disinclined to leave the gameplaysession while on the system 100. There are likely no breaks in thegameplay for anything that is not related to the system 100. In exampleembodiments, the system 100 may be configured so that one playing thegame will not accidentally click on an advertisement that may take theuser away from the system to something the user is not interested in.

In at least one example embodiment, it is anticipated that the averageuser will answer approximately 52 questions per session withapproximately 3 sessions per week. This equates to over 8,000 questionsper user per year and over 24,000 inputs per user per year. This equatesto approximately 66 hours of gameplay per user per year or approximately1 hour and 15 minutes per week. This, however, is not a limiting aspectof example embodiments as the average player may answer more or lessthan about 52 questions per session and may participate in more or lessthan 3 sessions a week.

The system 100 of example embodiments favors quality over quantity, bothin responses per user and users on the system 100. Users targeted forparticipation may fit the description of “important”.

The system of example embodiments may utilize a new method of questioninput and administrative oversight. In at least one example embodimentusers do not create questions. Only the owner, in some embodiments, maycreate questions. These questions may be intelligently created to buildon each other and relate to each other. That is, some questions may becreated with intentional correlations to one another and the variouscorrelations may be stored as data in the electronic database 25. Thisdata may be usable for helping to correct user input values with regardsto the alpha inputs and alpha data set as will be explained later.

In example embodiments, the questions may not be specific to onespecific company but rather to a market or industry. Questions may becreated with a thought towards creating a valuable data set. Thisrepresents a difference between the system and trivia games. Mostcompanies may not pay for a data set regarding a trivia question but maypay for a data set regarding business, politics or product marketingsuch as, whether employees prefer additional vacation or bonuses.Furthermore, the system 100 focuses on unknown questions whereas triviagames have only questions with known answers.

In example embodiments, when answering questions (i.e. providing alpha,beta, and zeta inputs) there may be a timer for each input and thetimers may be different for each question or input, but the timer maygenerally be around 20 seconds per input for a total of 60 seconds perquestion. It is anticipated that users may take any portion of the timeor all of the available time per question to answer. The timers may beintelligently designed to take into account the number of characters ina question, the number of potential answers, the number of characters inthe answers, and a manual input for the general difficulty of thequestion. Thus, for example, a timer for the question “Do most peoplerent or own: A) Rent or B) Own?” may be shorter than a timer for thequestion “What item is the most valuable item in an emergency: A)Shelter; B) Weapon; C) Cell phone; D) Money; or E) Food?”

Throughout the process of gameplay users may be able to “level up”which, in this application, may mean becoming an accredited player. Inexample embodiments, certain players may develop certain gaming habitswhich, while logical on a personal level, may be detrimental to the datagathering process as a whole. These users may be targeted to becomeaccredited players in the system 100. One can become an accreditedplayer by reaching a certain amount of wealth (for example, in Zeta),having a certain level of income for a certain period of time, ordemonstrating sufficient knowledge. This more advanced version ofgameplay keeps users interested and challenged when playing.

On the subject of knowledge, it is anticipated that there may besupplemental “tests” that are independent of the gameplay process. Thesetests may be conducted to measure knowledge and better understand users.These tests may be around ten questions and may be unlike otherquestions in the game and may have actual correct and incorrect answersand only one input per question which may include selection and time tomake selection based upon timer.

In example embodiments, expanded features may be available to accreditedplayers. The inventor, for example, anticipates that the system 100,which may include a monetary system, may help educate users on theconcepts of corporations, banks, and lending. In another exampleembodiment, the system 100 may allow for certain levels ofaccreditation. Upon becoming accredited players at a certain level usersmay have the opportunity to create corporations wherein users can poolassets and allow a single “manager” to make investments. Corporateshareholders, however, in one embodiment may not be able to see whatspecific questions the manager is answering and how the outcomes ofthose specific questions are distributed, however investors may be ableto monitor performance in certain sectors. Investing in a corporation inthis way may allow for passive income.

In example embodiments, a round of gameplay may consist of one questionand four screens provided to a user and the user may provide: 1) theuser's personal viewpoint and time it took to select viewpoint (alphainput); 2) the user's estimation of the popular viewpoint and time ittook to select popular viewpoint (beta input); 3) the amount of currencyrisked by the user and time it took to select amount of currency to berisked (zeta input); and 4) pause/product screen—where updated accountinformation may be presented, navigation features may be presented, anda sponsored product may be featured. In example embodiments, a new roundmay be started with a new question. These aspects of example embodimentsare not meant to limit the invention. For example, in exampleembodiments a user may not directly provide the times it took to providea personal viewpoint to a question, an estimation of the popularviewpoint, and to risk currency. Rather, these times may be obtained bythe system 100 which includes timers that time how long it took for theuser to enter each of the aforementioned data. Also, while the aboveexample illustrates an embodiment where the system 100 provides data ina manner that causes a user interface 10 to display a question acrossthree different screens the system 100 may alternatively be configuredto cause the user interface 10 to display a question on a single screenand allow the user to enter their personal viewpoint to a question, anestimation of the popular viewpoint, and an amount of currency to riskall on the single screen.

FIGS. 3 and 11 present methods which are substantially the same as FIGS.2 and 10, however, the methods of FIGS. 3 and 11 include an extra stepof presenting a user the pause/product screen. In example embodiments,the product screen may present a product available in the store managedby system 100. In example embodiments the product screen may beavailable after every round of input as shown in FIGS. 3 and 11,however, it is also envisioned that the product screens may be shownafter every other round of input, every third round of input, or afterany number of rounds of input the system owners deems appropriate. Thisoption may be important for users who are not interested in frequentlyseeing a product screen. In example embodiments this option may beprovided to the user and the user may define how often the productscreen is displayed or may be determined by the system owner.

FIGS. 4 and 12 present a method which is substantially the same as FIGS.3 and 11. However, the methods of FIGS. 4 and 12 include a step wherethe zeta speculated by a user may be removed from the user's account andput into an escrow account. In addition, the product/pause screen maygive a user an option of adding a product to a shopping cart or take theuser to a detailed product description. Furthermore, these methodsintroduce a method of categorizing alpha inputs. In these specificnonlimiting examples, the alpha inputs may be categorized based oncertain parameters and user habits and only some of the alpha inputs maybe used to determine the payout. In these particular nonlimitingexamples, alpha inputs may be categorized after all of the questionspresented to a user have been asked.

FIGS. 5 and 13 are further examples of a method in accordance withexample embodiments. These methods may be somewhat similar to themethods illustrated in FIGS. 4 and 12, however, in these nonlimitingexamples, the alpha input is categorized and stored in a database aftereach zeta input is provided.

FIGS. 6 and 14 are further examples of a method in accordance withexample embodiments. As shown in FIGS. 6 and 14, the alpha, beta, andzeta inputs produced by the user may be encrypted and stored in anelectronic database. Because encrypting and storing data is relativelywell known in the art, a detailed description thereof is omitted for thesake of brevity.

FIG. 7 is a view of an example method in which an alpha input providedby a user is classified into different categories. Although FIG. 7illustrates the alpha inputs as falling into one of six differentcategories, the number of categories may be more or less than six. Infact, the particular criteria illustrated in FIG. 7 is for the purposeof illustration only and may vary from one embodiment to another.

Referring to FIG. 7, the system 100 in accordance with exampleembodiments may determine whether or not a particular alpha input isclassified as “corrected input.” In this application the system 100 mayoffer a user an opportunity to correct an alpha input they may haveincorrectly entered. For example, if a user were asked “Do most peoplerent or own?” and the user entered “Rent” when they meant to enter“Own,” the system 100 allows the user to correct the alpha input. Thesystem 100, for example, may provide a “correct previous answer” optionor a “back” option to correct the mistake. In example embodiments, theability to correct a mistake may be for a limited amount of time, forexample, five (5) seconds or one (1) minute to keep the player engagedand to prevent the player from researching a question in order toprovide what they believe may deem is the correct answer to the questionpresented. In the event an alpha input is entered by a player and thencorrected, this input may be classified as “Type 6” input.

In example embodiments, it is anticipated that some users/players maylikely not read a question prior to providing an input. In the event thealpha input entered in response to a question was entered ratherquickly, for example, in less than a time period (for example, a presettime period of about three seconds), it may be likely the user did notread the question and simply made a selection. Thus, in this particularnonlimiting example, if the alpha input was not corrected data but thetime it took to enter the alpha input was less than three seconds, thisalpha input may be classified as “Type 5” data, or any other descriptoras the owner of the system 100 deems fit. Although the timing associatedwith this latter operation was provided as three seconds, the time isarbitrary and the time associated with this step could be another timeperiod such as two (2) seconds or five (5) seconds or any other timeperiod the owner of the system 100 deems is proper.

In example embodiments, the system 100 may further classify the alphainput as Type 1, Type 2, Type 3, or Type 4. In order to qualify as Type4 data an alpha input provided by a user for a particular question maybe the same as a beta input provided by the user for the particularquestion and the user may speculate a certain amount of zeta on theparticular question. In the event this occurs, a certain portion of thetime, say 90% of the time, this input may be classified as Type 4 input.

In the event the user's alpha and beta inputs are different for aparticular question the system 100 may then determine whether thequestion is a repeated question. If it is, the system 100 may determinewhether or not a repeat change in an answer can be true. If it cannotand the user provides a different answer to the same question, then thisalpha data may be saved as Type 3 data.

If the user does provide a consistent answer to a repeated question, orif the answer of a repeat question can differ and still be true, or ifthe question is not a repeat question, then the system 100 may determinewhether or not similar questions have previously been answered. If not,then the alpha data may be classified as Type 2, if yes, the data may beclassified as Type 1.

In example embodiments, a payout for any of the above methods may simplybe computed as a function of the alpha data set utilizing all alphainputs, or in another embodiment, as a function of only some of thealpha inputs comprising the alpha data set. For example, as shown inFIG. 7, the Type 1 data may be augmented or changed by a correlationfactor and a demographic correction. The correlation factor (CF), forexample, may be based upon correlations to similar questions. Forexample, in example embodiments, a user may be asked the followingquestion: “Which whiskey do you prefer?” Suppose further there are fivesimilar questions in the database, namely:

1) Do you drink alcohol? Y/N;2) What is your age range? A13-19/B20-29/C30-44/D45-60/E60+;3) Are you over 21? Y/N;4) What type of alcohol do you prefer? AGin/BRum/CTequila/DWhiskey; and5) Do you drink wine? Y/N.

These questions have different levels of similarity to the question“Which whiskey do you prefer?” and these different levels of similaritymay be determined by the question inputters/owner. In this particularnonlimiting example embodiment, the similarity of questions may beranked between 0 and 1. For example, the similarities the last fivequestions shared with the question “Which whiskey do you prefer?” may bedetermined by the inputter/system owner as:

1) 0.92) 0.53) 0.64) 0.65) 0.4

In this particular example, suppose the user only answered questions 1,2, 4, 5. The inputs from these particular questions may be analyzedfurther. For example, only type 1 and type 2 inputs may be used incomputing a correlation factor. Suppose further that only answers for 1,2, and 5 were of these types. Of these questions, each response may havea certain “corrected user input value” between 0 and 1, which again maybe established by a system owner or question inputter.

1) Y=1,N=0 2) A=0,B=1,C=1,D=1,E=1 3) Y=1,N=0 4) A=0.5,B=0.5,C=0.5,D=1 5)Y=1,N=0.3

Suppose further, the particular user selected 1) Y, 2) D, 5) N. In thiscase, the correlation factor may be computed as:((0.9×1)+(0.5×1)+(0.4×0.3))/(0.9+0.5+0.4)=1.52/1.8=0.84. It is notedthat the above is merely an example of how a correlation factor may becomputed and the invention is not limited by the instant example. As forthe demographic correction, each question may have a different desireddemographic. For example, the desired demographic for truck buyers maybe different from the desired demographic for compact hybrid car buyers.For example, the desired demographic for truck buyers may be defined asAmerican, Male, Over 20, Employed, Business Owner. The desireddemographic may generally be the demographic that is assumed to be themost likely of using a product or being impacted by the question. Inexample embodiments, it may be important to note how the demographiccorrection works with the Correction Factor (CF) which is essentiallythe behavioral attributes of the user.

In example embodiments, the demographic correction may be determined bythe owner and may be between 0 and 2, or any other range as the ownermay deem fit. If the user met all of the criteria they may receive afactor of 2. If the user only met a portion of the criteria theDemographic Correction (DC) may be computed by giving each attribute adifferent corrected user input value. For example, being male may bedeemed more important than being a business owner, so the genderreceives greater corrected user input valuing. In a similar way to theCF the demographic correction factor DF may use an averaging concept.

This demographic information may be stored for each user. An example ofthe corrected user input valueing for each desired group is shown below:

1) Country: America—0.8 2) Gender: Male—0.8 3) Age: Over 20-0.9 4)Employment Status: Employed—0.7 5) Business Owner: Y/N—0.3

Thus, if a user met all criteria the demographic factor may be computedas:

2((0.8×1)+(0.8×1)+(0.9×1)+(0.7×1)+(0.3×1)/(0.8+0.8+0.9+0.7+0.3)=2

However, in example embodiments, different factors may be used if a userdoesn't exactly match a specific demographic. For example, if the userwas Canadian, the demographic factor for the Canadian may be 0.9 ratherthan 0.8. This information may be important since many trucks are soldin Canada. In example embodiments, it is possible the system 100 may nothave a demographic for a particular user. In this case, the demographicinformation may simply have a factor of 0.0 or an arbitrarily smallnumber.

In example embodiments, the alpha inputs from a plurality of users willbe collected over a discrete period of time. After the discrete periodof time elapses, the question will not be presented to a user. Users mayonly be able to provide one alpha, beta, and zeta input per question.After the discrete period of time elapses the alpha inputs collectedfrom the plurality of users will be collected and a distribution ofalpha inputs for a question will be computed. In example embodiments,the payout for a user may be dependent on the type 1, 2, 3, 4, 5, and/or6 alpha inputs collected from the plurality of users and whether or nota player receives a payout may be dependent on their beta input. Forexample, suppose ten thousand users were presented with the question “Domost people rent or own?” Suppose further that of the ten thousand usersseven thousand's alpha input (type 1) was “own” and three thousand'salpha input (type 1) “rent.” Suppose further a user provided alpha inputof “rent,” but beta input of “own.” Suppose further, the ownerdetermined the payout should be based only on type 1 alpha inputs.Because the user's beta input matched the most popular type 1 alphainput, the user may win a certain amount of currency provided their zetainput for the question was not 0 or was not skipped.

FIG. 8 is a view of another method in accordance with exampleembodiments. Consistent with the earlier examples, the method of FIG. 8may include operations of allowing a user, via the user interface 10, toselect a topic, a subtopic, or another category such as popularquestions in order to cause the system 100 to provide a plurality ofquestions to the user. In this particular nonlimiting exampleembodiment, the system 100 may display a question to the user which maystart an alpha timer. The alpha timer may be a smart timer which mayvary from question to question depending on certain factors such as, butnot limited to, the number of characters in a question, the number ofpotential answers for a question, the number of characters in theanswers, the gameplay habits of the user, and/or the general difficultyof the question. In the method of FIG. 8, after the timer begins theuser may read the question, provide alpha input related to the question(for example, provide a personal viewpoint of the data). After the alphainput is provided the alpha timer may stop, the alpha data may beencrypted, and the alpha input, including selection and time to makeselection may be stored in an electronic database, for example, thedatabase 25 of system 100. In example embodiments it is understood thatif the user does not answer a question within a given time periodtracked by the alpha timer the question is withdrawn from the user'sconsideration and an “out of time” screen is presented to the user withthe option to continue wherein another question is presented to theuser.

In FIG. 8, the system 100, after the alpha input is provided by theuser, may redisplay the question. As soon as the question is redisplayedthe system 100 may start a beta timer and the user may then read thequestion and provide beta input. Providing the beta input stops the betatimer after which the beta input may be encrypted and stored in theelectronic database 25. In example embodiments, the beta timer, like thealpha timer, may be, but is not required to be, a smart timer which mayvary from question to question depending on certain factors such as, butnot limited to, the number of characters in a question, the number ofpotential answers for a question, the number of characters in theanswers, the gameplay habits of the user, and/or the general difficultyof the question. In example embodiments it is understood that if theuser does not answer a question within a given time period tracked bythe beta timer the question may be withdrawn from the user'sconsideration and another question is presented to the user.

In FIG. 8, the system 100, after the beta input is provided by the user,may redisplay the question a third time. As soon as the question isredisplayed the system 100 may start a zeta timer and the user may thenread the question and provide zeta input which may be currencyspeculated by the user and amount of time to make selection. Providingthe zeta data may stop the zeta timer after which the zeta input may beencrypted and stored in the electronic database 25. In exampleembodiments, the zeta timer, like the alpha timer, may be, but is notrequired to be, a smart timer which may vary from question to questiondepending on certain factors such as, but not limited to, the number ofcharacters in a question, the number of potential answers for aquestion, the number of characters in the answers, the gameplay habitsof the user, and/or the general difficulty of the question. In exampleembodiments it is understood that if the user does not answer a questionwithin a given time period tracked by the zeta timer the question may bewithdrawn from the user's consideration and another question may bepresented to the user.

In the method of FIG. 8, speculated/risked currency (for example, zeta)may be debited from a user's account and put into an escrow account.Gains or losses from previous speculations may be updated to the user'saccount so the user may learn the amount of currency the user may havefor speculation.

In the method of FIG. 8, the system 100 may cause the user interface 10to generate a product/pause screen after the zeta input is entered. Inexample embodiments, the user interface 10 may be configured to allowthe user to either prompt the system 100 to provide another question,provide a detailed description of the product, or add the product to alist for purchase/viewing at a later date.

Though not specifically shown in FIG. 8, the method classifying alphainput illustrated in FIG. 7 may be used classify to the alpha inputafter the alpha and beta inputs for a particular question have beenentered. Thus, the method of FIG. 8 may also include the method steps ofFIG. 7.

FIG. 9 is a view of a method of calculating a payout for a user/player.As indicated previously, a user may provide alpha input, beta input, andzeta input. A user utilizing the system 100 has an opportunity to riskcurrency and receive a payout. In order to receive a payout, the user'sbeta input must match a group's most popular alpha input, or a mostpopular alpha input in a type of alpha input. That is, the user mustcorrectly predict what the most popular and answer of a given questionis in order to receive a payout. In the example of FIG. 9, thiscorresponds to the most popular type 1 input provided by a plurality ofusers, however, it could have corresponded to the most popular type oftype 2, 3, 4, 5, or 6 input, or simply the most popular alpha input.Once the type 1 alpha input is collected from all users the type 1 alphadata set may be analyzed, the distribution is calculated, and the mostpopular answer is determined. A payout equation for calculating a payoutbased on the most popular answer and the distribution is used tocalculate a payout. If a particular user's beta input matched the mostpopular alpha input the user may receive a payout which may be amultiple of the zeta risked by the user and the user's account may becredited. If the user's beta input did not match the most popularanswer, the user may simply lose the risked zeta.

Example embodiments are not intended to be limited by the aforementionedmethods. For example, FIG. 15 illustrates a method of collecting andrefining data in accordance with another example embodiment. The instantexample embodiment may, for example, be implemented on system 100. Forexample, in this example embodiment the network system 20 may cause theuser interface 10 to display a question on a screen and start a timer.User may not be aware of timer. A user may provide alpha input for thequestion and the system 100 may stop the timer in response to the userproviding the alpha input. The alpha input and the time spent selectingthe alpha input for the question may be recorded in an electronicdatabase, for example, the electronic database 25. In this particularnonlimiting example the system 100 may classify the alpha data based onthe time the user spent answering the question. For example, a user whospent little time reading a question and providing input for thequestion may simply have found the question uninteresting and thereforedid not spend much time on his/her input. The user may have also notread the question and simply selected an input. This input may,therefore, be unreliable. As such, the system 100 may be configured suchthat if the user spent less than a predetermined or preset amount oftime (for example, one second, two seconds, or three seconds) answeringa question and providing the alpha input the alpha input may becategorized as a type 2 data. If, on the other hand, the user spent morethan the preset or predetermined amount of time answering the questionthe data may be stored as type 1 data. In this particular nonlimitingexample, the type 1 data may represent more accurate data and thereforemay be more desirable than the type 2 data. It is understood that otherrelevant refining processes may exist based upon the collection of Alphainput and time to select alpha input. Also, in this example embodiment,a payout may be calculated based on the type 1 data. Finally, in thenonlimiting example of FIG. 15 the user may request another question tocontinue the game play or end the game play.

FIG. 16 is another example of a method in accordance with exampleembodiments. The embodiment of FIG. 16 may be substantially similar tothat of FIG. 15 except that the embodiment of FIG. 16 includes an extradata input related to how strongly a user feels regarding the alphainput. For example, the system 100 may, in addition to providing aquestion to the user, request the user indicate how strongly they feelthe input provided was correct. This could be in the form of a slidingscale or a number ranging from 1 to N with N indicating very confidentand 1 indicating not very confident. In this particular example, N maybe any number deemed appropriate by the system owner. For example, N maybe 1, 2, 5, or 10.

FIG. 17 illustrates another example of a method in accordance withexample embodiments. As in the previously described embodiments, themethod of FIG. 17 may be implemented on the system 100. For example, inthe method of FIG. 17, the network 20 may cause the user interface 10 todisplay a question and the user may enter both alpha and beta inputregarding the question. This alpha and beta input may be stored in anelectronic database, for example, the database 25 of system 100. Thealpha and beta input may not include a time to select input. Finally, inthe nonlimiting example of FIG. 17 the user may request another questionto continue the game play or end the game play.

FIG. 18 illustrates another example embodiment. As in the previouslydescribed embodiments, the method of FIG. 18 may be implemented on thesystem 100. For example, in the method of FIG. 18, the network 20 maycause the user interface 10 to display a question and the user may enterboth alpha and beta input regarding the question. This alpha and betainput may be stored in an electronic database, for example, the database25 of system 100. Furthermore, in the method of FIG. 18, the user may beprompted to provide information as how confident they are in their betainput and this input may be saved in the electronic database. This couldbe in the form of a sliding scale or a number ranging from 1 to N with Nindicating very confident and 1 indicating not very confident. In thisparticular example, N may be any number deemed appropriate by the systemowner. For example, N may be 1, 2, 5, or 10. Finally, in the nonlimitingexample of FIG. 18 the user may request another question to continue thegame play or end the game play.

Example embodiments of the invention have been described in anillustrative manner. It is to be understood that the terminology thathas been used is intended to be in the nature of words of descriptionrather than of limitation. Many modifications and variations of exampleembodiments are possible in light of the above teachings. Therefore,within the scope of the appended claims, the present invention may bepracticed otherwise than as specifically described.

What we claim is:
 1. A method of collecting data comprising: asking a plurality of questions to a plurality of users and obtaining a plurality of alpha, beta, and zeta inputs from the plurality of users; computing question payouts for each user based on a distribution of alpha data; and crediting a player's account the computed payouts.
 2. The method of claim 1, wherein computing question payouts includes determining a most popular answer within an alpha data set; and determining whether a user's beta input matches the most popular answer within the alpha data set and crediting a player's account includes crediting a player's account when the player's beta input matches the most popular answer within the alpha data set.
 3. The method of claim 2, further comprising; categorizing the alpha input, wherein the distribution of alpha data is performed on at least one, but not all, of the categorized of alpha data.
 4. The method of claim 1, further comprising: calculating a correlation factor and augmenting a user's alpha input with the correlation factor.
 5. The method of claim 4, further comprising: calculating a demographic correction and augmenting the user's alpha input with the demographic correction.
 6. The method of claim 5, wherein the correlation factor and demographic correction are calculated only for one type of alpha input.
 7. The method of claim 1, further comprising: providing a user an option to purchase a product.
 8. The method of claim 1, further comprising: tracking the times it takes for each player to provide alpha, beta, and zeta inputs.
 9. The method of claim 7, wherein tracking the times it takes for each player to provide an answer is tracked by a digital timer.
 10. The method of claim 1, further comprising: placing at least some of a user's currency in an electronic escrow account while a payout is calculated.
 11. A computer system having a nontransitory computer readable-medium configured to: present a plurality of questions to a plurality of users and obtain a plurality of alpha, beta, and zeta inputs from the plurality of users; compute question payouts for each user based on a distribution of alpha data; and credit a player's account the computed payouts.
 12. The system of claim 11, wherein computing question payouts includes determining a most popular answer within an alpha data set; and determining whether a user's beta input matches the most popular answer within the alpha data set and crediting a player's account includes crediting a player's account when the player's beta input matches the most popular answer within the alpha data set.
 13. The system of claim 12, wherein the system is further configured to: categorize the alpha input, wherein the distribution of alpha data is performed on at least one, but not all, of the categorized of alpha data.
 14. The system of claim 11, wherein the system is further configured to: calculate a correlation factor and augmenting a user's alpha input with the correlation factor.
 15. The system of claim 14, wherein the system is further configured to: calculate a demographic correction and augment the user's alpha input with the demographic correction.
 16. The system of claim 15, wherein the correlation factor and demographic correction are calculated only for one type of alpha input.
 17. The system of claim 11, wherein the system is further configured to: provide a user an option to purchase a product.
 18. The system of claim 11, wherein the system is further configured to: track the times it takes for each player to provide alpha, beta, and zeta inputs.
 19. The system of claim 17, wherein the system includes electronic timers configured to track the times it takes for each player to provide an answer.
 20. The system of claim 11, further comprising: an electronic database configured to escrow at least some of a user's currency to an while a payout is calculated. 